In [1]:
pwd
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In [2]:
cd C:\Users\alpha\Documents\work\burned pixels\ecoregions_countries_climates_continents
In [15]:
import geopandas as gpd
import matplotlib.pyplot as plt
import pickle
import pandas as pd
import numpy as np
import calendar
import matplotlib.cm as cm
import matplotlib.colors as cls
from mpl_toolkits.axes_grid1.inset_locator import inset_axes
In [4]:
#latex
import matplotlib
matplotlib.rcParams['mathtext.fontset'] = 'custom'
matplotlib.rcParams['mathtext.rm'] = 'Times New Roman'
matplotlib.rcParams['mathtext.it'] = 'Times New Roman:italic'
matplotlib.rcParams['mathtext.bf'] = 'Times New Roman:bold'
matplotlib.rcParams["text.latex.preamble"].append(r'\usepackage{faktor}')
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clima = gpd.read_file('clima')
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clima.columns
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In [18]:
clima['SUM_Area'].head()
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In [19]:
data_columns = clima.columns[2:2+17*12]
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data_columns
Out[20]:
In [21]:
inter_cols = {}
for yr in range(17):
inter_cols[yr] = data_columns[12*yr:12*yr +12]
In [22]:
area = clima['SUM_Area']*1e-6
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intra_cols = {}
for mo in range(12):
intra_cols[mo] = [data_columns[mo + yr *12 ] for yr in range(17)]
In [24]:
for yr in range(17):
inter = clima[ inter_cols[yr]].sum(axis=1)/area
label = 'inter_' + str(yr +2001)
clima= clima.assign(label=inter)
clima = clima.rename(columns={'label': label})
In [25]:
clima.plot(column='inter_2001', cmap='OrRd')
Out[25]:
In [26]:
def draw_map(df,column,title=None,vmin=0,vmax=1,name='name',dpi=100):
fig, ax = plt.subplots(figsize=(12, 8))
ax.set_aspect('equal')
ax = df.plot(ax =ax, column=column, cmap='OrRd',vmin=vmin, vmax=vmax)
ax.set_axis_off()
norm = cls.Normalize(vmin, vmax)
cmmapable = cm.ScalarMappable(norm, 'OrRd')
cmmapable._A = []
cbaxes = inset_axes(ax, width="80%", height="1%", loc=3)
cb = plt.colorbar(cmmapable, cax=cbaxes,orientation="horizontal")
cb.set_label(title, fontsize=20, family='Times New Roman')
cb.ax.set_yticklabels(cb.ax.get_yticklabels(), fontsize=15, family='Times New Roman')
plt.show()
fig.savefig(name, dpi=dpi, bbox_inches='tight')
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inter_columns = clima.columns[-17:]
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inter_max = clima[inter_columns].max().max()
In [29]:
for mo in range(12):
intra = clima[ intra_cols[mo]].sum(axis=1)/(17*area)
label = 'intra_{:02}'.format(mo)
clima = clima.assign(label=intra)
clima = clima.rename(columns={'label': label})
In [30]:
intra_columns = clima.columns[-12:]
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intra_max = clima[intra_columns].max().max()
In [32]:
clima.plot(column='intra_01', cmap='OrRd')
Out[32]:
In [82]:
#draw_map(df,column,title=None,vmin=0,vmax=1,name='name',dpi=100)
column = 'intra_{:02}'.format(1)
mo_name = calendar.month_name[1+ 1]
title = r' Burned Area Pixels/$\mathrm{Km}^{2}$ by climate 2001-2017 ' + mo_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'TESTEintra_ecoregions_{}'.format(calendar.month_name[mo + 1].lower())
draw_map(clima,column,title=title,vmin=0,vmax=intra_max,name=name)
In [83]:
#draw_map(df,column,title=None,vmin=0,vmax=1,name='name',dpi=100)
column = 'inter_{}'.format(2001)
mo_name = calendar.month_name[1+ 1]
title = r' Burned Area Pixels/$\mathrm{Km}^{2}$ by climate 2001-2017 ' +'2001'
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'TESTEinter_ecoregions_{}'.format(2001)
draw_map(clima,column,title=title,vmin=0,vmax=inter_max,name=name)
Inter-Anual¶
In [84]:
%%time
for yr in range(17):
column = 'inter_{}'.format(yr +2001)
yr_name = str(yr +2001)
#"Burnt area pixels (#/km2) by biome in 2001
title = r'Burned area pixels ($\#/\mathrm{km}^{2}$) by climate in ' +yr_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'inter_climas_{}'.format(yr +2001)
draw_map(clima,column,title=title,vmin=0,vmax=inter_max,name=name)
Intra_Anual¶
In [87]:
%%time
for mo in range(12):
column = 'intra_{:02}'.format(mo)
mo_name = calendar.month_name[mo + 1]
#"Burnt area pixels (#/km2) by biome in Janury (2001-2017)"
title = r'Burned area pixels ($\#/\mathrm{km}^{2}$) by climate in ' +mo_name + ' (2001-2017)'
#title = r' Burned Area Pixels/$\mathrm{Km}^{2}$ by climate 2001-2017 ' + mo_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'intra_climas_{:02}'.format(mo)
draw_map(clima,column,title=title,vmin=0,vmax=intra_max,name=name)
In [89]:
data_columns
Out[89]:
In [155]:
total = clima[data_columns].sum(axis=1)/17
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total = total/area
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total.plot()
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In [158]:
clima= clima.assign(total=total)
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clima.plot(column='total',cmap='OrRd')
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In [103]:
total.max()
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In [104]:
total.min()
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In [105]:
clima.plot(column='total',cmap='OrRd', vmin=0,vmax=total.max())
Out[105]:
In [108]:
clima.plot(column='total',cmap='OrRd', scheme='quantiles')
Out[108]:
In [109]:
column = 'total'
title = r' Burned Area Pixels/$\mathrm{Km}^{2}$ by climate 2001-2017 '
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'total_climas'
draw_map(clima,column,title=title,vmin=0,vmax=total.max(),name=name)
In [129]:
column = 'total'
title = r' Burned Area Pixels/$\mathrm{Km}^{2}$ by climate 2001-2017 '
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'total_climas'
draw_map(clima,column,title=title,vmin=0,vmax=total.max(),name=name)
testes¶
In [135]:
yr =0
column = 'inter_{}'.format(yr +2001)
yr_name = str(yr +2001)
#"Burnt area pixels (#/km2) by biome in 2001
title = r'Burned area pixels ($\#/\mathrm{km}^{2}$) by climate in ' +yr_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'inter_climas_{}'.format(yr +2001)
draw_map(clima,column,title=title,vmin=0,vmax=inter_max,name=name)
In [37]:
yr =0
column = 'inter_{}'.format(yr +2001)
yr_name = str(yr +2001)
#"Burnt area pixels (#/km2) by biome in 2001
title = r'Burned area pixels ($\#/\mathrm{km}^{2}$) by climate in ' +yr_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'inter_climas_{}'.format(yr +2001)
draw_map(clima,column,title=title,vmin=0,vmax=inter_max,name=name)
In [41]:
clima.columns[-20:]
Out[41]:
In [76]:
from scipy.stats import boxcox
In [89]:
test_column = boxcox(clima.inter_2001 + 1e-25)
In [84]:
any(clima.inter_2001 + 1e-10 <=0)
Out[84]:
In [88]:
clima.inter_2001 + 1e-25
Out[88]:
In [91]:
clima.inter_2001.size
Out[91]:
In [98]:
test_column[0]
Out[98]:
In [ ]:
clima[inter_columns].min().min()
In [99]:
clima = clima.assign(test_column =test_column[0])
In [100]:
yr =0
column = 'test_column'
yr_name = str(yr +2001)
#"Burnt area pixels (#/km2) by biome in 2001
title = r'Burned area pixels (#/$\mathrm{km}^{2}$) by climate in ' +yr_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'teste'
draw_map(clima,column,title=title,vmin=test_column[0].min(),vmax=test_column[0].max(),name=name)
In [102]:
clima.plot(column='inter_2001',cmap='OrRd' ,scheme='quantiles')
Out[102]:
In [12]:
yr_name = '2001'
title = r'Burned area pixels ($\mathrm{\#}/\mathrm{km}^{2}$) by climate in ' +yr_name
In [13]:
plt.text(0.1,0.5,title);
In [175]:
!set PATH=%PATH%;C:\Program Files\MiKTeX 2.9\miktex\bin\x64
In [5]:
!path
In [128]:
lambdas = []
for yr in range(17):
column= 'inter_{}'.format(2001 + yr)
test_column, lbd = boxcox(clima[column] + 1e-25)
lambdas.append(lbd)
lmbda= sum(lambdas)/len(lambdas)
In [129]:
maxs = []
mins = []
for yr in range(17):
column= 'inter_{}'.format(2001 + yr)
test_column = boxcox(clima[column] + 1e-25,lmbda=lmbda)
maxs.append(test_column.max())
mins.append(test_column.min())
In [140]:
lambdas
Out[140]:
In [130]:
maxs
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In [132]:
mins
Out[132]:
In [133]:
box_min = min(mins)
box_delta =max(maxs) - box_min
In [136]:
test_column = (boxcox(clima.inter_2001 + 1e-25,lmbda=lmbda) - box_min)/box_delta
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test_column
Out[137]:
In [138]:
clima = clima.assign(test_column =test_column)
In [139]:
yr =0
column = 'test_column'
yr_name = str(yr +2001)
#"Burnt area pixels (#/km2) by biome in 2001
title = r'Burned area pixels (#/$\mathrm{km}^{2}$) by climate in ' +yr_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'teste'
draw_map(clima,column,title=title,vmin=0,vmax=1,name=name)
com a aldrabice de deslocar o x uma unidade resulta algo semelhante a não transformar nada¶
Inter-anual¶
In [141]:
for yr in range(17):
column = 'inter_{}'.format(yr +2001)
test_column = (boxcox(clima[column] + 1e-25,lmbda=lmbda) - box_min)/box_delta
clima = clima.assign(test_column =test_column)
yr_name = str(yr +2001)
#"Burnt area pixels (#/km2) by biome in 2001
title = r'Burned area index (#/$\mathrm{km}^{2}$) by climate in ' +yr_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'inter_climas_index_{}'.format(yr +2001)
draw_map(clima,'test_column',title=title,vmin=0,vmax=1,name=name)
Intra-anual¶
In [142]:
lambdas = []
for mo in range(12):
column= 'intra_{:02}'.format(mo)
test_column, lbd = boxcox(clima[column] + 1e-25)
lambdas.append(lbd)
lmbda= sum(lambdas)/len(lambdas)
In [143]:
lambdas
Out[143]:
In [144]:
lmbda
Out[144]:
agora os lambdas estão longe de ser tão próximos o que traduz que a variação intra-anual é mais acentuada¶
In [145]:
maxs = []
mins = []
for mo in range(12):
column= 'intra_{:02}'.format(mo)
test_column = boxcox(clima[column] + 1e-25,lmbda=lmbda)
maxs.append(test_column.max())
mins.append(test_column.min())
In [146]:
box_min = min(mins)
box_delta =max(maxs) - box_min
In [153]:
for mo in range(12):
column = 'intra_{:02}'.format(mo)
test_column = (boxcox(clima[column] + 1e-25,lmbda=lmbda) - box_min)/box_delta
clima = clima.assign(test_column =test_column)
mo_name = calendar.month_name[mo + 1]
#"Burnt area pixels (#/km2) by biome in Janury (2001-2017)"
title = r'Burned area index (#/$\mathrm{km}^{2}$) by climate in ' +mo_name + ' (2001-2017)'
#title = r' Burned Area Pixels/$\mathrm{Km}^{2}$ by climate 2001-2017 ' + mo_name
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'intra_climas_index_{:02}'.format(mo)
draw_map(clima,'test_column',title=title,vmin=0,vmax=1,name=name)
In [149]:
clima[column]
Out[149]:
In [150]:
lmbda
Out[150]:
In [152]:
(boxcox(clima[column] + 1e-25,lmbda=lmbda) - box_min)/box_delta
Out[152]:
In [159]:
column = 'total'
test_column, lbd = boxcox(clima[column] + 1e-25)
test_column = (test_column - test_column.min())/(test_column.max() - test_column.min())
clima = clima.assign(test_column =test_column)
title = r'Burned area index (#/$\mathrm{km}^{2}$) by climate in (2001-2017)'
#title = r'Burned Area Pixels/$\mathrm{Km}^{2}$ by Climate 2001-2017 {} '.format(calendar.month_name[mo + 1])
#title =(r'ABC123 vs $\mathrm{ABC123}^{123}$')
name = 'total_climas_index'
draw_map(clima,'test_column',title=title,vmin=0,vmax=1,name=name)
In [163]:
from scipy.stats import probplot, norm
In [168]:
probplot(clima.intra_11, dist=norm, plot=plt.gca());
In [169]:
probplot(clima.test_column, dist=norm, plot=plt.gca());
